Abstract
As peer-to-peer (P2P) technology booms lots of problems arise such as rampant piracy, congestion, low quality etc. Thus, accurate identification of P2P traffic makes great sense for efficient network management. As one of the optimal classifiers, support vector machine (SVM) has been successfully used in P2P traffic identification. However, the performance of SVM is largely dependent on its parameters and the traditional tuning methods are inefficient. In the paper, a novel hybrid method to optimize parameters of SVM based on cuckoo search algorithm combined with particle swarm optimization algorithm is proposed. The first stage of the proposed approach is to tune the best parameters for SVM with training data. Subsequently, the SVM configured with the best parameters is employed to identify P2P traffic. In the end, we demonstrate the effectiveness of our approach on-campus traffic traces. Experimental results indicate that the proposed method outperforms SVM based on genetic algorithm, particle swarm optimization algorithm and cuckoo search algorithm.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Steinmetz, R., Wehrle, K.: Peer-to-Peer Systems and Applications. Springer, Berlin (2005)
Constantinou, F., Mavrommatis, P.: Identifying known and unkonwn peer-to-peer traffic. In: Fifth IEEE International Symposium on Network Computing and Application, pp. 93–102. IEEE Press, Cambridge, MA (2006)
Kim, M.-S., Kang, H.-J., Hong, J.W.: Towards Peer-to-Peer Traffic Analysis Using Flows. In: Brunner, Marcus, Keller, Alexander (eds.) DSOM 2003. LNCS, vol. 2867, pp. 55–67. Springer, Heidelberg (2003)
Bleul, H., Rathgeb, E.P.: A Simple, efficient, and flexible approach to measure multi-protocol peer-to-peer traffic. In: Lorenz, P., Dini, P. (eds.) Networking - ICN 2005. LNCS, vol. 3427. Springer, Heidelberg, pp. 606–616 (2005)
Sen, S., Spatscheck, O., Wang, D.: Accurate, scalable in-network identification of P2P traffic using application signatures. In: WWW2004, pp. 512–521. ACM Press, New York (2004)
Won, Y.J., Park, B.-C, Ju, H.-T.: A hybrid approach for accurate application traffic identification. In: 4th IEEE/IFIP Workshop on End-to-End Monitoring Techniques and Services, 2006, pp. 1–8. IEEE Press, Canada (2006)
Xu, K., Zhang, M., Ye, M., Chiu, D.M., Wu, J.: Identify P2P traffic by inspecting data transfer behavior. Comput. Commun. 33, 1141–1150 (2010)
Keralapura, R., Nucci, A., Chuah, C.N.: A novel self-learning architecture for P2P traffic classification in high speed networks. Comput. Netw. 54, 1055–1068 (2010)
Liu, B.: A semi-supervised clustering approach for P2P traffic classification. J. Netw. 6(3), 424–431 (2011)
Moore, A.W., Zuev, D.: Internet traffic classification using Bayesian analysis techniques. In: Proceedings of ACM Sigmetrics, pp. 50–60 (2005)
Jin, F., Duan, Y.: A P2P flow identification model based on Bayesian network. In: 7th International Conference Wireless Communications, Networking and Mobile Computing (WiCOM), 2011, pp. 1–4. IEEE Press, Wuhan (2011)
Chen, H., Hu, Z., Ye, Z.:. Research of P2P traffic identification based on neural network. In: IEEE Conference on Computer Network and Multimedia Technology, 2009, pp. 18–20. IEEE Press, Wuhan (2009)
Chen, H., Zhou, X., You, F., Xu, H., Wang, C., et al.: A SVM approach for P2P traffic identification based on multiple traffic mode. J. Netw. 5(11), 1381–1388 (2010)
Zheng, J., Xu, Y.: Identification of network traffic based on support vector machine. In: 2010 3rd International Conference on Advanced Computer Theory and Engineering, pp. 286–291. IEEE Press, Chengdu (2010)
VapNik, V.N.: The Nature of Statistical Learning Theory. Springer, New York (1995)
Eberhart, R.C., Shi, Y.: Comparison between genetic algorithms and particle swarm optimization. In: Proceedings of 1998 7th Annual Conference on Evoluationary Programming, vol. 1447, pp. 611–616. Springer, Berlin, Heidelberg (1998)
Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proceedings of IEEE International Conference on Neural Networks, 1995, pp. 1942–1948. IEEE Press, Piscataway (1995)
Esmin, A., Torres, G., Zambroni, A.: A hybrid particle swarm optimization applied to loss power minimization. IEEE Trans. Power Syst. 20(2), 859–866 (2005)
Ting, T.O.: A novel approach for unit commitment problem via an effective hybrid particle swarm optimization. IEEE Trans. Power Syst. 21(1), 11–418 (2006)
Parimala, R.: Feature selection using a novel particle swarm optimization and It’s variants. I.J. Inf. Technol. Comput. Sci. 5, 16–24 (2012)
Yang, X., Deb, S.: Cuckoo search via levy fights. In: 2009 World Congress on Nature and Biologically Inspired Computing (NaBIC 2009), India, pp. 210–215. IEEE Press, Coimbatore (2009)
Civicioglu, P., Besdok, E.: A conceptual comparison of the cuckoo-search, particle swarm optimization, differential evolution and artificial bee colony algorithms. Artif. Intell. Rev. 39, 315–346 (2013)
Wang, F., Luo, L., He, X., Wang, Y.: Hybird optimization algorithm of PSO and Cickoo search. In: IEEE 2011 2nd International Conference on Artificial Intelligence, Management Science and Electronic Commerce (AIMSEC), pp. 1172–1175. IEEE Press, Deng Leng (2011)
Boser, B.E., Guyon, I.M., Vapnik, V.N.: A training algorithm for optimal margin classifiers. In: 5th Annual ACM Workshop on COLT, pp. 144–152. ACM Press, New York (1992)
Acknowledgment
This work is supported by Natural Science Foundation of China (No. 41301371, 61170135 and 61202287), the Emergency Management Program for National Natural Science Foundation of China (No. 61440024), Doctor Fund of Hubei University of technology (BSQD13081 BSQD12032).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Ye, Z., Wang, M., Wang, C., Xu, H. (2015). P2P Traffic Identification Using Support Vector Machine and Cuckoo Search Algorithm Combined with Particle Swarm Optimization Algorithm. In: Zhang, S., Xu, K., Xu, M., Wu, J., Wu, C., Zhong, Y. (eds) Frontiers in Internet Technologies. ICoC 2014. Communications in Computer and Information Science, vol 502. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-46826-5_10
Download citation
DOI: https://doi.org/10.1007/978-3-662-46826-5_10
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-662-46825-8
Online ISBN: 978-3-662-46826-5
eBook Packages: Computer ScienceComputer Science (R0)